Ageism in Hiring: The Influence of Implicit Cues on Perceptions of Candidate Warmth, 

Competence, Suitability, and Trainability 

 
By 

Morgan Greene 

July, 2025  

 Director of Thesis: Dr. Courtney Baker  

Major Department: Department of Psychology   

ABSTRACT   

This study investigates how implicit age cues in resumes and perceived technology ability 

influence hiring evaluations. Drawing from the Stereotype Content Model (Fiske et al., 2002), 

we examine how these cues affect perceptions of warmth and competence, which in turn shape 

judgments of trainability and suitability. In an experimental design with 588 participants, 

resumes were manipulated to include subtle age indicators and technology skill levels. Results 

showed that perceived technology ability significantly predicted warmth in some models, though 

this effect was not consistently observed across all analyses. Implicit age cues and their 

interaction did not significantly predict warmth or competence. Warmth did not significantly 

predict trainability or suitability, nor did it mediate the effects of age-related cues on outcomes. 

Competence significantly predicted both trainability and suitability, reinforcing its primacy in 

task-oriented evaluations. This supports prior findings that competence stereotypes play a central 

role in age-based hiring decisions (Cuddy et al., 2008; Hashim & Wok, 2013). Additionally, 

raters’ ageist beliefs predicted lower suitability and competence ratings, but higher trainability, 

suggesting complex prescriptive biases in candidate evaluations (North & Fiske, 2013b). These 

findings underscore how subtle cues and evaluator biases shape early hiring judgments and 



   
 

highlight the need for more structured, bias-reducing hiring practices (Derous & Decoster, 2017; 

Green et al., 2020). 

  



   
 



   
 

 

 

AGEISM IN HIRING: THE INFLUENCE OF IMPLICIT CUES ON PERCEPTION OF 

CANDIATE WARMTH, COMPETENCE, SUITABILITY, AND TRAINABILITY 

 

A Thesis   

Presented to the Faculty of the Department of Psychology   

East Carolina University   

   

In Partial Fulfillment of the Requirement for the Degree  

Master of Arts in Psychology   

   

by   

Morgan Greene 

July 2025 

 

Director of Thesis: Courtney L. Baker, Ph.D.  

Thesis Committee Members: 

 Kent K. Alipour, Ph.D.   

Alexander M. Schoemann, Ph.D. 

 



   
 

 
 

 

 

 

 

 

 

 

 

© Morgan A. Greene, 2025



   
 

 

Acknowledgements  

 

I would like to express my deepest gratitude to my thesis chair, Dr. Courtney L. Baker, for her 

unwavering support and mentorship throughout this process. Thank you for teaching me the art 

of formal academic writing and for encouraging my growth as a researcher. I am also deeply 

appreciative of Dr. Kent K. Alipour for sharing his insights into industrial-organizational 

psychology through the lens of business and leadership. Your guidance has profoundly shaped 

my perspective on the field, and your mentorship and support have been invaluable in my growth 

as a student and emerging professional. Additionally, I want to thank Dr. Alexander M. 

Schoemann for his invaluable guidance in statistical methods and data analysis. Your thoughtful 

feedback and patient instruction have been essential to my understanding of complex 

methodologies, and your encouragement has pushed me to refine my work and approach each 

challenge with confidence. Finally, I extend my heartfelt thanks to everyone who has been part 

of my journey, offering encouragement, guidance, and support. This process has been a 

remarkable learning experience, and I am deeply grateful for the lessons I have gained along the 

way. 

 



   
 

 

Table of Contents 

Title Page........................................................................................................................................i 

Acknowledgements .......................................................................................................................iii 

List of Tables.................................................................................................................................vii 

List of Figures...............................................................................................................................viii 

List of Symbols and Abbreviations ...............................................................................................ix 

CHAPTER I: INTRODUCTION ....................................................................................................1 

CHAPTER 2: LITERATURE REVIEW………………………………………………...……......7 

Implicit Age Cues and Stereotype Content Model .........................................................................7 

Hypothesis 1…………………………………………………………………………………...…11 

Perceived Technology Ability…………………...........................................................................11 

Hypothesis 2a…………………………………………………………………………………….13 

Hypothesis 2b………………………………………………………………………………….…13 

Implicit Age Cues & Suitability/Trainability Ratings…...............................................................13 

Hypothesis 3…………………………………………………………………………………...…14 

Technology Ability & Implicit Age Cues with Suitability/Trainability Ratings...........................15 

Hypothesis 4…………………………………………………………………………………..….15 

Warmth & Competence to Trainability /Suitability…………………………………………...…15 

Hypothesis 5a…………………………………………………………………………………….17 

Hypothesis 5b…………………………………………………………………………………….17 

Hypothesis 5c………………………………………………………………………………….…17 

Mediation of Model…….…………………..................................................................................17 

Hypothesis 6……………………………………………………………………………………...17 



   
 

CHAPTER 3: METHOD …..........................................................................................................19 

Participants.....................................................................................................................................19

Procedure & Design......................................................................................................................21 

Measures .......................................................................................................................................22 

Perceived Warmth…......................................................................................................................22 

Perceived Competence ……..........................................................................................................22 

Perceived Trainability....................................................................................................................23 

Perceived Suitability………………………………………………………………………...…...23 

Succession, Identity, Consumption Prescriptive Ageism Scale……………………………….....23 

CHAPTER 4: RESULTS ……......................................................................................................26 

Descriptive Statistics………………………………………………………………….………….26 

Hypotheses Testing………………………………………………………………………………29 

CHAPTER 5: DISCUSSION……………………………………………………………….……41 

Warmth and Competence in Hiring Evaluations….…….…………………………………….....41 

Age and Ageism Perceptions ……………………………………………………...…………….43 

Theoretical Implications …………………………………………………..….…...…………….44 

Practical Implications ……………………………………………………………....................…47 

Limitations & Future Research Directions…………….……………………………………...…49 

Conclusion………………………………………………………………………….……………53 

References ….................................................................................................................................55 

Table 1……….…..........................................................................................................................68 

Table 2……….…..........................................................................................................................69 

Table 3……….…..........................................................................................................................70 



   
 

Table 4……….…..........................................................................................................................71 

Table 5……….…..........................................................................................................................72 

Table 6……….…..........................................................................................................................73 

Table 7...........................................................................................................................................74 

Table 8...........................................................................................................................................75 

Table 9...........................................................................................................................................76 

Table 10.........................................................................................................................................77 

Table 11.........................................................................................................................................78 

Table 12.........................................................................................................................................79  

Table 13.........................................................................................................................................80 

Figure 1……….….........................................................................................................................81 

Appendices …................................................................................................................................82 

Appendix A …...............................................................................................................................82 

Appendix B …...............................................................................................................................83 

Appendix C …...............................................................................................................................85 

Appendix D …...............................................................................................................................94 

Appendix E …...............................................................................................................................95 

 

 



   
 

 

List of Tables 

Table 1...........................................................................................................................................68 

Table 2...........................................................................................................................................69 

Table 3...........................................................................................................................................70 

Table 4...........................................................................................................................................71 

Table 5...........................................................................................................................................72 

Table 6...........................................................................................................................................73 

Table 7...........................................................................................................................................74 

Table 8...........................................................................................................................................75 

Table 9...........................................................................................................................................76 

Table 10.........................................................................................................................................77 

Table 11.........................................................................................................................................78 

Table 12.........................................................................................................................................79 

Table 13.........................................................................................................................................80  



   
 

List of Figures  

Figure 1..........................................................................................................................................8



   
 

List of Abbreviations & Symbols 

Stereotype Content Model (SCM)...................................................................................................8 

Mean (M).......................................................................................................................................19 

Sample Size (n)..............................................................................................................................19 

Age Discrimination in Employment Act (ADEA).........................................................................22  

Cronbach’s Alpha (α).....................................................................................................................23 

McDonald’s Omega (ω).................................................................................................................23 

Coefficient of Determination (𝑅 2)................................................................................................30  

Level (p).........................................................................................................................................33 

Confidence Interval (CI)................................................................................................................33 

Mean (M).......................................................................................................................................19 

Standard Deviation (SD)................................................................................................................32 

Indirect Effect (a*b).......................................................................................................................34 

Degrees of Freedom (df)................................................................................................................61 

Unstandardized Beta (b)................................................................................................................63 

Standard Error (SE)........................................................................................................................63  

F-Statistic (F).................................................................................................................................66 

Institutional Review Board (IRB)..................................................................................................98 



Chapter 1:  

INTRODUCTION 

Age-based hiring discrimination affects both workers and organizations. For workers, it 

may lead to career stagnation, early retirement, or unemployment (Batinovic et al., 2023). For 

organizations, it risks lawsuits, reduced diversity, and challenges from an aging workforce 

(Kunze et al., 2011; Hebl et al., 2020; Batinovic et al., 2023). Age discrimination is illegal under 

U.S. law, particularly the Age Discrimination in Employment Act (1967), which prohibits 

discriminatory practices against individuals aged 40 and older (Stypinska & Turek, 2017). 

However, research has suggested that discrimination is still prevalent within hiring processes 

(Bovenkerk, 1992; Harris, 2022; Abrams et al., 2016; Derous & Decoster, 2017; Van Borm et 

al., 2021). 

Notably, at least 48% of the population reports experiencing ageism (Kim et al., 2015; 

Wilson et al., 2019). The older workers are those 40 and older who are protected by the Age 

Discrimination in Employment Act (1967). Those who are younger are not protected by this act 

(Stypinska & Turek, 2017).This is especially concerning as older workers are the fastest-growing 

group in the global workforce (Batinovic et al., 2023). There is some evidence suggesting that 

unemployment after job loss is higher for older individuals than for younger ones (Khele et al., 

2012). This could be due to outdated job skills and requirements, and explicit or implicit cues 

favoring the hiring of younger workers (Derous & Decoster, 2017; Abrams et al., 2016; Ahmed 

et al., 2012; Berger, 2005; Patrickson & Ranzijn, 2003). 

Employers can infer a candidate’s age through explicit cues (e.g., graduation year, date of 

birth) or implicit cues (e.g., name, activities; Kleissner et al., 2021). While explicit cues reveal 

age directly, implicit cues, like hobbies and names, provide indirect signals. This implies that



2 

 

   
 

recruiters can make inferences about age unconsciously, which impacts the hiring rates of older 

workers (Kleissner et al., 2021; Derous & Decoster, 2017). Previous research on explicit and 

implicit age cues in resumes shows a consistent bias toward hiring younger candidates over older 

ones (Abrams et al., 2016; Derous & Decoster, 2017; Kleissner et al., 2021; Van Borm et al., 

2021). 

Furthermore, there has been limited research addressing implicit age cues, despite calls 

for further exploration of implicit age cues within the hiring process (Abrams et al., 2016; 

Derous & Decoster, 2017). Implicit cues are seen as "hard to fake" because they go against one’s 

conscious control, noting unobserved qualities (Connelly et al., 2011; Bangerter et al., 2012; 

Derous & Decoster, 2017). This differentiates them from explicit cues that are purposefully 

placed on a resume to show the recruiter a quality the candidate presumes (Abrams et al., 2016; 

Derous & Decoster, 2017). I aim to fill this gap in literature by exploring implicit age cues 

exclusively within the first step of the hiring process (i.e., resume screening), to examine 

unconscious mechanisms (i.e., perceived warmth and competence) by raters that indicate biases 

based on age, which can subsequently impact perceptions of their trainability and suitability. 

Furthermore, this study will also determine whether perceived technology ability may mitigate 

the impact of implicit age cues. 

Even the most professional, well-trained HR personnel can be influenced by implicit 

biases and rely on their "gut feelings." The bias in resume screening begins with the judgments 

made when viewing a candidate’s resume, and these perceptions are seldom entirely objective or 

neutral (Harris, 2022). As many as 90% of all discriminatory decisions are made before the 

interview (i.e., when looking at a resume; Bovenkerk, 1992; Harris, 2022). This assertion is 

particularly true when it comes to age-related stereotypes and job-related discriminatory factors 



3 

 

   
 

(i.e., trainability, physical ability, retention of candidates) (Abrams et al., 2016; Derous & 

Decoster, 2017). 

Implicit cues in hiring may lead to subjective judgments about the character of the 

applicant (Abrams et al., 2016; Derous & Decoster, 2017). Drawing on the stereotype content 

model (Cuddy et al., 2008), older workers are perceived as warm but less competent than 

younger workers (Krings et al., 2011). This is due to stereotypes suggesting that older workers 

are less proficient but are seen to have kinder or pity emotions when rated by others (Cuddy et 

al., 2008; Krings et al., 2011; Finkelstein, 2014). Considering that recruiters often have limited 

information about candidates (e.g., age, gender, education level, and work experience; Van Borm 

et al., 2021), they may rely on a few characteristics—such as perceived warmth or competence 

(Cuddy et al., 2008)—which can lead to biased hiring decisions. 

Older workers often face disadvantages in the hiring process compared to their younger 

counterparts, receiving fewer callbacks, interviews, and job offers (Kleisser et al., 2021). 

Theoretical frameworks emphasize unconscious biases in decision-making processes, where 

individuals may unknowingly favor younger applicants over older ones (Levy & Banaji, 2002). 

Older workers frequently do not fit the "stereotypical worker" image, where younger white males 

are perceived as warm and competent, making it easier to justify discrimination against them 

(Cuddy et al., 2008; Krings et al., 2011). 

Differences in perceptions of younger and older workers stem from stereotypes 

associated with each group. Prior research using the Stereotype Content Model (SCM) suggests 

that older workers are often viewed as having strong mentoring abilities, organizational 

commitment, warmth, and reliability (Karpinska et al., 2013). However, older workers are often 

stereotyped in a pervasive and negative manner (e.g., technophobic, unable to be trained, etc.; 



4 

 

   
 

Kroon et al., 2018; Finkelstein et al., 2013; Ng & Feldman, 2012). These perceptions are often in 

direct contradiction with the ways that a "stereotypical worker" would be perceived. As a result, 

those engaged in hiring decisions may be more likely to use implicit cues within job materials to 

unintentionally discriminate against older workers. Prior research shows that older age signals to 

recruiters that the candidate has lower technology skills, flexibility, and trainability levels (Van 

Borm et al., 2021). Thus, implying that older workers would be seen as more friendly and warm 

based on personality but less trainable and less suitable than younger workers for the job listed. 

I investigated how this age bias impacts the perceived trainability and suitability for the 

job position listed. I proposed the nuanced idea that older candidates and the use of technology 

based on age (i.e., younger candidates would be inexperienced, and older candidates would be 

more experienced) would impact ratings for accepting candidates for the job description. 

Furthermore, I explored implicit age cues within a resume (i.e., names, skills, and social media 

presence) and the impact of these cues on the ability to receive a position (i.e., suitability and 

trainability) based on counterstereotypes. 

Younger workers are stereotyped to have higher technology ability than older workers. 

This could be due to their ability to adapt to recent technology quickly or the vast technological 

shifts over the past 10 years (Brooke & Taylor, 2005). Based on this stereotype, I manipulated 

the conditions to reflect going against the prescriptive age identity stereotype (i.e., older implicit 

age candidates would have good technology ability) to see if job suitability ratings would change 

based on the violation of prescriptive stereotyping of age (Hanrahan et al., 2022). Prescriptive 

stereotypes suggest younger individuals view themselves as ideal, tech-savvy users, while older 

users are often stereotyped as technophobic or unskilled (Comunello et al., 2020; Comunello et 

al., 2017; Neves & Amaro, 2012). Research has shown that older people who experience 



5 

 

   
 

stereotype threat in the technological domain are associated with lower levels of technology use 

among older adults (Mariano et al., 2022). This study aimed to explore individuals who do not 

fall within the stereotypical realm, highlighting how individuals who defy age-based 

technological stereotypes are perceived in terms of job suitability and competence. 

Previous research found that those who went against prescriptive stereotypes (e.g., 

young-sounding name and enjoys knitting) received the lowest job suitability ratings (Derous & 

Decoster, 2017). By focusing on both older workers who exhibit high levels of technological 

proficiency and younger workers who may be less skilled with technology, the current research  

investigated whether challenging these stereotypes can lead to biases within the recruitment 

process. While stereotypes can drive discriminatory practices, some organizations are 

implementing measures such as structured interviews, blind resume screening, and bias training 

to address these issues (Green et al., 2020; Buijsrogge et al., 2021). This research aims to inform 

and support these efforts by identifying specific instances of stereotype-driven bias in 

recruitment. 

This study makes two contributions: first, it advances research on implicit age cues in 

workplace discrimination (Derous & Decoster, 2017; Abrams et al., 2016; Kleissner et al., 2021). 

While explicit cues are widely studied, implicit cues remain underexplored (Kleissner et al., 

2021). Given that implicit cues are "hard to fake," it can be expected that raters would 

demonstrate strong classification effects when evaluating job suitability and trainability, 

particularly when reviewing four resumes: one for a young candidate, one for an older candidate, 

one for an older candidate with strong tech skills, and one for a younger candidate with limited 

tech skills (Bangerter et al., 2012; Abrams et al., 2016; Derous & Decoster 2017). Most resumes 

contain both explicit age cues (e.g., year graduated) and implicit cues (i.e., self-reported 



6 

 

   
 

characteristics such as flexibility). Previous research has concluded that explicit age cues 

confirm age biases in the workplace (Abrams et al., 2016; Derous & Decoster 2017). Prior 

research has called for nuanced investigations of implicit age cues within the hiring process 

(Derous & Decoster 2017; Abrams et al., 2016; Kleissner et al., 2021; Foley & Williamson, 

2018); this study can also provide the framework for understanding why these perceptions are so 

prevalent in older age categories by utilizing the Stereotype Content Model (Fiske et al., 2002; 

Cuddy et al., 2008) during the hiring process. 

The second contribution I make is the exploration of a single implicit age cue (i.e., tech 

ability) as a driving factor for trainability and suitability for the job position. Implicit age cues 

often work unconsciously, leading hiring managers to unknowingly rely on age and technology 

stereotypes, which may compromise fair and accurate judgments (Abrams et al., 2016; Derous & 

Decoster, 2017). This can address whether age-based stereotypes about technology skill (e.g., 

younger individuals being seen as more tech-savvy, and older individuals as less capable with 

technology) unduly influence decisions in hiring and evaluation processes. This manipulation is 

being made to identify and potentially correct biases that could lead to discrimination based on 

age and to promote a more accurate, skill-based evaluation that better reflects a candidate’s 

actual capabilities rather than relying on outdated or oversimplified stereotypes. Ultimately, it is 

about increasing fairness in hiring and making the process more inclusive and reflective of 

individual qualifications, rather than assumptions tied to age. 



Chapter 2 

LITERATURE REVIEW   

Implicit Age Cues and Stereotype Content Model 

Age can be perceived in the resume screening process in two separate ways, explicit age 

cues and implicit age cues. Importantly, age discrimination in the hiring process can occur 

because of explicit age cues that are consciously processes which convey the exact age of the 

candidate (Kleissner & Jahn, 2021). Implicit age cues are less studied; they are best defined as an 

indirect unconscious perception of the candidate listed. These can be seen in a resume as age-

stereotypic characteristics or activities listed in applicant profiles (e.g., name, skills; Kleissner et 

al., 2021). Some examples of explicit age cues in resumes are graduation dates or dates of birth 

(Kleissner et al., 2021). Implicit cues in resumes are exhibited in ways such as, names, skills, and 

hobbies; Kleissner & Jahn, 2020; Derous & Decoster, 2017). 

Previous research suggests that indirect, unconscious age cues can significantly influence 

hiring decisions (Van Borm et al., 2021; Kleissner et al., 2021). Specifically, younger workers 

tend to be favored over older workers when resumes include both explicit (e.g., date of birth) and 

implicit cues (e.g., hobbies or name associations) (Abrams et al., 2016; Derous & Decoster, 

2017; Martine et al., 2021). Derous and Decoster (2017) found that implicit age cues, such as 

younger-sounding names paired with older-associated hobbies, received the lowest hire ability 

ratings. These findings suggest that deviations from stereotypical expectations may trigger bias 

in evaluations. A study by Abrams et al. (2016) examined how age-based stereotypes influence 

hiring decisions, finding that employers favored younger candidates over older ones, despite 

having the same qualifications. Both studies highlight that prescriptive stereotypes lead to a 

preference for younger candidates. 



8 

 

   
 

The Stereotype content model (SCM) defines two fundamental dimensions of social 

perception. Warmth is defined as friendly, trustworthy, and caring (Fiske et al., 2002) and 

competence is defined as the capability to perform tasks given (Hashim & Wok, 2012). These 

constructs are influenced by implicit cues linked to both competition and status, potentially 

driven by stereotypical reasons. Combinations of warmth and competence generate distinct 

emotions of admiration, contempt, envy, and pity (Fiske et al., 2002; Cuddy et al., 2008). Krings 

et al., (2011). investigated if warmth- and competence-related stereotypical inferences mediate 

the relation between candidate age and selection bias. Results showed that age bias was robust. 

Older candidates were discriminated against during the selection process, even if the job 

primarily required warmth-related qualities, and independently of evaluators’ own age or 

professional experience in human resources. 

According to the Stereotype Content Model (SCM), older adults are generally perceived 

as high in warmth but low in competence. This stereotype aligns with societal narratives 

portraying older individuals as kind, nurturing, and non-threatening (e.g., the "sweet old lady" or 

"kind grandfather"), while simultaneously being viewed as less capable, slower, or less 

competent due to age-related cognitive or physical declines. Research supports this pattern: 

Cuddy, Norton, and Fiske (2005) found that older individuals were perceived as warm but not 

competent, reinforcing a paternalistic stereotype. Similarly, Fiske et al. (2002) identified that 

groups perceived as high in warmth but low in competence often elicit pity rather than 

admiration or envy. Ageism research further suggests that people may unconsciously devalue 

older workers’ abilities, assuming they are less adaptable, innovative, or productive (Posthuma & 

Campion, 2009). 

 



9 

 

   
 

Older workers are perceived as warm but less competent than younger workers (Krings et 

al., 2011). This is due to prescriptive stereotypes that older workers are less competent but are 

seen to have kinder or reflect pity emotions when rated by others (Fiske et al., 2002; Cuddy et 

al., 2008; Krings et al., 2011; Finklestein, 2014). This stems from the idea that older workers are 

not portrayed as the “stereotypical workers” (e.g., younger white males are seen as warm and 

competent) therefore, making it easier to justify discrimination among them. (Fiske et al., 2002; 

Cuddy et al., 2008; Krings et al., 2011). Prior research using the Stereotype Content Model 

(SCM) has found that older workers are portrayed as having good mentoring qualities, 

commitment to the organization, and warm personalities and reliability (Karpinska et al., 2013). 

However, older workers are negatively portrayed as more expensive, technologically less savvy, 

less productive and less adaptable to training processes (Kroon et al., 2018). This implies that 

older workers would be perceived as more friendly and warm based on personality but less 

competent (i.e. technologically less savvy) than younger workers. 

Older workers encounter discriminatory challenges in the labor market due to prevalent 

beliefs about their capabilities. While they are often viewed as more reliable, trustworthy, and 

loyal, they are simultaneously perceived as less adaptable, less motivated, and less competent 

compared to their younger counterparts (van Selm & van den Heijkant, 2021). Research 

indicates a widely held perception that older employees struggle more than younger ones to meet 

the demands of today’s complex and competitive workplaces. Consequently, organizations tend 

to allocate fewer resources for the training and education of older workers (Boerlijst et al., 1994; 

Van der Heijden et al., 2009; Van Dalen et al., 2010). In essence, older employees face 

workplace discrimination rooted in stereotypical beliefs regarding their abilities (Bal et al., 2011; 



10 

 

   
 

Conen et al., 2011; Perry et al., 2017; Posthuma & Campion, 2009; Van der Heijde & Van der 

Heijden, 2006; Weiss & Perry, 2020).   

In an experimental context where individuals rate older-sounding names (e.g., "Walter" 

or "Gertrude") on perceived warmth and competence, these stereotype-driven expectations could 

shape judgments, leading to higher warmth and lower competence ratings for perceived older 

workers. This implicit bias may, in turn, affect perceptions of job suitability and trainability, 

reinforcing age-related disparities in hiring and professional development opportunities. 

Van Borm et al. (2021) examined the signals a candidate's age conveys to employers 

during resume screening. The study found that older candidates are perceived by recruiters as 

having lower technological skills and less flexibility. Additionally, the probability of receiving 

an interview invitation decreases with age, particularly in firms with a lower percentage of older 

employees. While stigmas against older workers contribute to age-related hiring discrimination, 

it is also crucial to explore the stereotypes and biases that impact younger workers in the labor 

market.  

Stereotypes about younger workers are often overlooked in the literature, despite 

evidence that 28% of younger workers report experiencing age discrimination (Schmitz et al., 

2023; Raymer et al., 2017). The limited research on younger workers suggests significant 

consequences, including lower job satisfaction, reduced work engagement, and increased work 

interference (Schmitz et al., 2023; Rabl & Kühlmann, 2009). While stereotypes about older 

workers are predominantly negative, stereotypes about younger workers tend to be more positive 

(Perry et al., 2013).  

Common age stereotypes suggest that younger workers are more productive, creative, 

ambitious, eager, and efficient than older workers (Toomey & Rudolph, 2017; Perry et al., 2013). 



11 

 

   
 

They are also perceived as better at handling job stress, more likely to seek immediate feedback, 

and often viewed as entitled, tech-savvy, multitaskers who value work-life balance (Toomey & 

Rudolph, 2017; Perry et al., 2013). Field experiments indicate that when explicit age markers are 

present on resumes, recruiters tend to prefer younger applicants over older ones (Lahey, 2008; 

Albert et al., 2011; Krings et al., 2011; Ahmed et al., 2012; Derous & Decoster, 2017). 

According to stereotype content models (SCM), younger workers are seen as less warm but more 

competent than their older counterparts. 

H1: The candidate with the older age implicit cue would be perceived as warmer 

and less competent in comparison to the candidate with the younger age implicit 

cue.   

Perceived Technology Ability 

The importance of technological skills in a rapidly evolving job market has grown 

significantly over the past decade (Cirillo, 2017). Van Borm (2021) found that age signals to 

recruiters lower technological ability, accounting for 41% of the likelihood of being invited to a 

job interview. This perception-driven reality has contributed to the unemployment of older 

workers due to technological changes and innovation (Brynjolfsson & McAfee, 2014; Cirillo, 

2017).  

Research indicates that older adults face greater challenges with the ever-changing 

technological demands of modern job descriptions, partly due to spatial processing limitations 

(Czaja & Lee, 2009; Tames et al., 2014; Charness et al., 2018). Additionally, many technological 

devices are designed with younger users in mind, featuring small screens, tiny buttons, and 

frequent interface changes (Ivan & Cutler, 2021; Birkland, 2022; Comunello et al., 2023). This 



12 

 

   
 

fosters a perception of the "ideal user" as young, white, and male for roles requiring advanced 

technological skills (Comunello et al., 2023). 

The concept of the "ideal user" extends beyond device design to influence workers' 

perceptions, particularly those of younger employees (Loos et al., 2012). Younger workers often 

view themselves as technologically savvy and prefer using technology for communication 

(Toomey & Rudolph, 2017; Comunello et al., 2020). In contrast, older workers are frequently 

perceived as technophobic and possessing limited skills (Neves & Amaro, 2012; Comunello et 

al., 2017). Exploring the intersection of technology and age, Martine (2021) examined how 

technology-driven positions could be adapted for older workers. Recruiters interviewed in the 

study expressed a sentiment that older candidates are generally less digitally savvy or rely on 

outdated knowledge compared to younger colleagues. Furthermore, research suggests that these 

stereotypes about older workers' technological abilities often align with their self-perceptions 

(Mariano et al., 2022; Birkland, 2022). 

Birkland (2022) examines how aging stereotypes impact older adults' use of technology. 

The study finds that older adults (e.g., over 65) are often perceived as warm but incompetent, 

particularly in their use of modern technologies like computers, smartphones, and the internet. 

Older adults are aware of these stereotypes, which portray them as ‘frustrated,’ anxious,’ or 

‘slow’ learners of advanced technologies (Birkland, 2022). Based on the SCM, this implies that 

younger workers would be perceived as less warm but more competent when it comes to 

technological systems (Fiske et al., 2002; Cuddy et al., 2008).  

Self-perceptions of older workers and technological skills were evaluated by Mariano et 

al., (2022) the authors found that older people tend to use technology less due to stereotype threat 

of confirming ageist stereotypes targeting their age group. This indicates that since technological 



13 

 

   
 

ability implies age (e.g., the elderly are seen as warmer and less competent) within the SCM, 

people would tend to stay within the bounds of their grouped stereotypes. An extension to SCM 

is prescriptive stereotype theory that indicates that stereotypes are not just descriptive (i.e., 

perceived by others) but they also dictate how groups should behave (North & Fiske, 2013a, 

2013b; Hanrahan et al., 2023). In this case, it would be socially unacceptable for those who are 

older to have higher technological skill than those who are younger, which could elicit a hostile 

emotion from younger workers who witness those in violation of the prescriptive stereotype 

(e.g., North & Fiske, 2013a, 2013b, 2017; Schoemann & Branscombe, 2010; Hanrahan et al., 

2023).  

H2a: The candidate with perceived technology skills would be perceived as less 

warm and more competent in comparison to the candidate without the perceived 

technology skills.  

H2b: Implicit age cues and perceived technology skills would interact to influence 

perceptions of warmth and competence. Specifically, candidates with an older 

implicit age cue who are perceived as having high technology skills would be 

evaluated less positively in terms of warmth and competence compared to those with 

lower perceived technology skills.  

Implicit Age Cues & Suitability/Trainability Ratings 

In a similar study conducted in a European country, Derous and Decoster (2017) found 

that resumes with younger-sounding names and more modern activities received the highest job 

suitability ratings. Resumes with older-sounding names and traditional activities followed 

closely behind, supporting the prescriptive stereotypes based on age perceptions (North & Fiske, 

2013a, 2013b; Hanrahan et al., 2023). Resumes with younger-sounding names and old-fashioned 



14 

 

   
 

activities received the lowest suitability scores, likely due to the violation of prescriptive 

stereotypes, making them appear less desirable (Hanrahan et al., 2023). 

Recent research by Van Borm et al. (2021) examined age signals to recruiters and found 

that older age signals a perception of lower trainability compared to younger workers. This 

finding aligns with other research that highlights a correlation between workers' age and their 

willingness to participate in training and career development. Older workers have been shown to 

be less willing to engage in such opportunities, which is the only consistent stereotype about 

older workers supported by empirical evidence (Ng & Feldman, 2012; van Selm & van den 

Heijkant, 2021).  

Based on the results of these studies, I predict my findings would align with previous 

research on perceptions of older workers and their ratings for job suitability and trainability. As 

noted by Abrams et al. (2016), Derous & Decoster (2017), and Martine et al. (2021), younger 

workers are often rated more highly than older workers, based on assumptions from the 

stereotype content model (SCM)—such as perceived warmth and competence (Fiske et al., 

2002). Additionally, when older workers violate prescriptive stereotypes (e.g., North & Fiske, 

2013a, 2013b, 2017; Schoemann & Branscombe, 2010; Hanrahan et al., 2023), these constructs, 

combined with implicit age cues on resumes, likely contribute to lower ratings for older 

candidates. 

H3: The candidate with the older age implicit cue would be perceived as less 

trainable for the position and less suitable for the position comparison to the 

candidate with the younger age implicit cue.   

Technology Ability & Implicit Age Cues with Suitability/Trainability Ratings 



15 

 

   
 

 The research previously mentioned has examined stereotypes associated with older 

workers, such as being less technologically adept or difficult to train (Kroon et al., 2018; Van 

Borm et al., 2021). In contrast, younger workers are often perceived as having contrasting traits, 

being more adaptable, easily trainable, and proficient with technology (Toomey & Rudolph, 

2017; Perry et al., 2013). These perceptions (SCM) can affect the overall ratings of trainability 

and suitability for the job position listed (Abrams et al., 2016; Deorus & Decoster, 2017; Martine 

et al., 2021). Other than older and younger worker stereotypes, there are other employment 

factors as to why a candidate with higher technology skills would receive higher ratings than 

those who have less technology ability (Cirillo, 2017).  

Hirvonen et al., (2022), found that advanced technologies led to an increase of 

employment. A previous study (Li, 2022) suggests that in five years from now, over two-thirds 

of the technology skills considered important in today’s job requirements will change (e.g., use 

of Artificial Intelligence). By 2025, "Technology use and control" and "Technology 

programming" are projected to rank seventh and eighth among top job skills, despite technology 

skills being absent from the list in 2015 and 2020 (Grey, 2016; Whiting, 2020). This highlights 

the rapid shift in technological skill demand (Comunello, 2017; Cirillo, 2017; Li, 2022). 

H4: The candidate with perceived technology skills would be perceived as more 

trainable for the position and more suitable for the position in comparison to the 

candidate without the perceived technology skills.  

Warmth & Competence to Trainability / Suitability  

The SCM shows that if you are older, you are more likely to be seen as not competent, 

but warm. Whereas younger people are seen as less warm but more competent. (Fiske et al. 

2002). A Previous study (Collange et al., 2009) conducted showed the difference of warmth and 



16 

 

   
 

competence on levels of job suitability for the job position. Findings show that the person 

perceived as warmer (e.g., working mother) scored higher suitability ratings than the candidate 

who appeared less warm (e.g., Asian American). The study also showed a positive relationship 

between warmth and judged suitability; this is also consistent with the findings of Lin et al., 

(2005). 

Cuddy et al. (2004) conducted a study to determine whether working mothers or childless 

mothers were perceived as more desirable candidates for assignments, promotions, and training 

opportunities. They found that while participants considered the warmth of a candidate when 

assessing their trainability for the job, warmth did not significantly influence training 

recommendations. However, drawing from the Stereotype Content Model (SCM), individuals 

perceived as warmer are generally viewed as more cooperative, approachable, and adaptable 

traits that contribute to their perceived ability to learn new skills and integrate into workplace 

environments (Fiske, 2002). As a result, candidates who are seen as warmer may be judged as 

more receptive to instruction and feedback, reinforcing the expectation that they be perceived as 

trainable. 

Hashim and Wok (2013) examined the competence, performance, and trainability of 

older workers in higher educational institutions in Malaysia, finding that contrary to prescriptive 

stereotypes, older workers were both competent and trainable. Their study also demonstrated a 

positive relationship between competence and trainability across different staff categories. 

Similarly, Periáñez-Cañadillas et al. (2019) explored the impact of digital competencies on job 

suitability for business graduates, hypothesizing that digital communication skills would enhance 

a candidate’s perceived job fit. Their findings supported this hypothesis, revealing that four out 

of five assessed competencies significantly predicted job suitability. Building on these empirical 



17 

 

   
 

findings, we propose that while warmth positively influences job suitability, it may not be related 

to perceived trainability. This suggests that a candidate’s warmth and friendliness enhance 

perceptions of their suitability for a role but do not necessarily impact judgments of their ability 

to be trained effectively. 

H5a: There would be a positive relationship between warmth and suitability. 

H5b: There would be a positive relationship between warmth of candidate and 

trainability. 

H5c: There would be a positive relationship between competence and trainability 

and suitability. 

Mediation of Model  

Implicit age cues may trigger immediate perceptions of a candidate’s suitability and 

trainability; however, these judgments may also be shaped by perceived warmth and 

competence, as outlined in the Stereotype Content Model. As such, candidates who are seen as 

both warm and competent may receive higher evaluations of suitability and trainability, 

influenced by unconscious biases related to age cues or technology skills (high vs. low) (Fiske, 

2002; Cuddy et al., 2008; North & Fiske, 2013a). For example, an older candidate with high 

perceived warmth may be rated higher on suitability, whereas a younger candidate demonstrating 

high competence might receive higher trainability ratings (Abrams et al., 2016; Hashim & Wok, 

2012). 

H6a: Perceptions of candidate warmth and competence mediate the relationship 

between implicit age cues and candidate outcomes, such that implicit age cues 

influence perceived warmth and competence, which in turn affects ratings of 

candidate trainability and suitability 



18 

 

   
 

H6b: The indirect effects of implicit age cues on candidate trainability and 

suitability via warmth and competence are moderated by perceived technology 

skills, such that the strength of these indirect effects varies depending on the level of 

perceived technology skills. Specifically, when perceived technology skills are high, 

the indirect effects through warmth and competence are attenuated. 



 

Chapter 3 

METHOD 

Sample and Data Preparation 

Cloud Research Connect was used to provide a controlled environment to recruit 

participants who met specific eligibility criteria, ensuring high-quality data and demographic 

diversity. The survey was conducted via Qualtrics and integrated with Cloud Research Connect 

for seamless participant recruitment and data collection. Participants were required to be at least 

18 years old, proficient in English, employed full-time (i.e., working at least 30 hours per week) 

in the United States, and pass embedded attention check questions to ensure data quality. A 

specific industry was not targeted in order to increase generalizability by capturing a range of 

industries and job levels. Participants who failed attention checks were excluded from the final 

analysis. 

Initial data cleaning included removing duplicate IP addresses (n = 17), responses 

missing IP data (n = 2), participants who did not complete the informed consent item (n = 3), and 

those with missing responses across all survey items (n = 12). Survey completion times were 

screened, and participants who completed the survey in less than one minute were excluded as 

likely careless responders (n = 14). Additionally, responses exceeding 45 minutes (i.e., more than 

two standard deviations above the mean; n = 45, M = 19.69, SD = 11.31) were excluded due to 

concerns about inattention or poor data quality. 

Attention check items were recoded for consistency. While 98 participants missed one or 

more attention checks, only two participants failed all three. Consistent with recommended 

practices (Meade & Craig, 2012), only those who failed all embedded attention checks were 



20 

 

   
 

excluded. This decision also aligns with concerns raised by Hauser et al. (2018), who argue that 

attention checks can act as interventions themselves—altering participants’ engagement or 

responses in unintended ways—and should therefore be used cautiously in determining data 

validity. Participants (n=588) provided demographic information including their age, 

organizational tenure, job type, gender, race/ethnicity, and average number of hours worked per 

week. The sample was composed of 54.4% individuals who identified as female, 42.2% as male.  

Racial and ethnic identification was diverse, with the majority identifying as White 

(62.8%). Other reported identities included Black or African American (9.0%), Asian (7.5%), 

Hispanic or Latino (5.6%), Native American or Alaska Native (1.4%), Middle Eastern or North 

African (0.9%), Pacific Islander (0.3%), and Other (1.4%). Multiracial identifications were also 

present in the sample (e.g., White and Black, White and Hispanic), reflecting 8.2% of the 

responses. The majority (61.2%) of participants reported working 40 or more hours per week, 

followed by 36.9% who reported working 30–40 hours. A smaller percentage reported working 

20–30 hours (1.5%) or 10–20 hours (0.3%). 

Occupations ranged with the highest frequencies in industries related to Computer and 

Mechanical (13.95%), Building and Grounds Cleaning and Maintenance (10.20%), Other 

(9.18%), Education, Training, and Library (8.84%), and Office and Administrative Support 

(7.31%). Less common occupations included Protective Services (0.34%), Farming, Fishing, and 

Forestry (0.34%), and Military (0.17%). In terms of job tenure, the number of months 

participants reported working at their current job ranged from 0 to 420 months (M = 6.92 years, 

SD = 7.16). The sample represented a broad age distribution. Participant ages ranged from 19 to 

73 years, with an average age of 41.55 years (SD = 10.83), suggesting a predominantly mid-

career population. 



21 

 

   
 

Procedure & Design  

This study employed a 2 (Implicit Age Cue: Young, Old) x 2 (Technology Skill: Yes, 

No) vignette experimental design. Participants were randomly assigned to one of four 

experimental conditions, which involved manipulated résumés that varied on two key factors: (1) 

implicit age cues (indicating whether the applicant was perceived as younger or older) and (2) 

the presence or absence of technology skills. These manipulations were based on prior research 

(Derous & Decoster, 2017), with the added nuance of technology ability (see Appendix C for 

specific manipulations). The resumes were crafted to subtly convey the age of the candidate 

through implicit cues, such as younger (e.g., Chase) or older (e.g., Earl) perceived names (Social 

Security, n.d.), while the high (e.g., Advanced expertise in Microsoft Office Suite) or low (e.g., 

Familiar with Microsoft Office) technology skills was conveyed through listed proficiencies at 

the end of the résumé. This design allowed for a systematic examination of the impact of these 

factors on participant evaluations. 

Following recruitment (see Appendix A), participants first reviewed and agreed to an 

informed consent document before proceeding (see Appendix B). They were then presented with 

one of the manipulated résumés (see Appendix C) and asked to rate the candidate on various 

attributes, including perceived warmth (Fiske, 2002), competence (Fiske, 2002), suitability for 

the position (Fox et al., 1995), and trainability (Wrenn & Maurer, 2004; see Appendix C). 

Demographic information (e.g., age, gender, race) was also collected (see Appendix C for 

surveys). Manipulation checks were included to assess the validity of the vignettes (see 

Appendix C). Validity checks were embedded throughout the battery of measures, prompting 

participants to respond to items with prescribed answers. For example, one validity check asked 

participants to respond “Disagree” to a specific item (Meade & Craig, 2012). After completing 



22 

 

   
 

the survey and reviewing the debrief (see Appendix D), participants were monetarily 

compensated $2.25 for their participation. 

Manipulation Checks 

To assess whether participants accurately perceived the candidate’s implicit age and 

technology ability cues, two manipulation checks were included. For the age manipulation, 

participants responded to the question, “What age do you think this candidate is?” with five 

response options: Under 30, 30–39, 40–49, 50–59, and 60 or older. Responses were categorized 

as a pass if they aligned with the intended age cue embedded in the resume. Specifically, 

candidates in the Young condition (coded as 1) if participants selected Under 30 or 30–39 

(options 1 or 2), while candidates in the Old condition (coded as 0) were participants selected 

40–49, 50–59, or 60 or older (options 3, 4, or 5). This categorization aligns with the legal 

definition of older workers under the Age Discrimination in Employment Act (ADEA), which 

protects individuals aged 40 and older from employment discrimination (ADEA, 1967). To 

represent older and younger individuals in our materials, we selected names based on popular 

baby names from the 1950s and 2000s, respectively, using data from the Social Security 

Administration (Social Security Administration, n.d.). 

For the technology ability manipulation, participants rated the candidate’s technological 

ability on a 5-point scale, ranging from Very Low (1) to Very High (5). Responses were 

categorized if they corresponded to the intended technology level of the resume. Specifically, 

candidates in the Low Tech condition (coded as 0) if participants selected Very Low or Low 

(options 1 or 2), while candidates in the High Tech condition (coded as 1) if participants selected 

High or Very High (options 4 or 5). This approach captures the intended differentiation between 



23 

 

   
 

resumes designed to signal either technological competence or a lack thereof, consistent with the 

study’s focus on implicit skill-based stereotypes. 

Measures  

Perceived Warmth (See Appendix C). Warmth is assessed with items adapted from 

(Fiske, 2002). This measure is used to show how friendly a candidate is perceived to be. The 

measure uses a 5-point Likert scale from (1) strongly disagree to (5) strongly agree. This 

measure uses descriptive words that can be perceived by the rater, that are followed by “To what 

extent do you consider the target to be…” some examples of this are “Good-Natured" or 

“Benevolent.” Harahan et al., (2023) adapted the Fiske, (2002) scale and had high internal 

reliability for the warmth scale (α = .90). In the present sample, the warmth scale showed strong 

internal consistency (α = .83, ω = .90). 

Perceived Competence (See Appendix C). Competence is assessed with items adapted 

from Fiske, (2002). This measure is used to show how capable a candidate is perceived to be. 

The measure uses a 5-point Likert scale (1) strongly disagree to (5) strongly agree. This measure 

uses descriptive words that can be perceived by the rater, that are followed by “To what extent 

do you consider the target to be…” some examples of this are “Efficient" or “Intelligent.” Fiske 

and colleagues (2002) demonstrated across nine samples that warmth and competence accounted 

for variability in stereotype content for 25 targeted groups. Hanrahan et al., (2023) had a high 

reliability for this competence scale (α = .95). In the present study, the competence scale 

demonstrated acceptable internal consistency (α = .70, ω = .87).   

Perceived Trainability (See Appendix C). This measure is assessed using items adapted 

from Wrenn and Maurer (2004) to evaluate the extent to which participants perceive a candidate 

as trainable for the specified position. It employs a 5-point Likert scale ranging from (1) Strongly 



24 

 

   
 

Disagree to (5) Strongly Agree. Example items include: “This employee could improve his 

performance with additional training” and “This employee would learn as much as any other 

employee from additional training.” Items 3, 4, and 6 were reverse coded. In the current study, 

the trainability scale demonstrated acceptable internal consistency (α = .75) and strong total 

reliability (ω = .83), supporting its use as a reliable measure of perceived trainability. 

Motive to Learn Measure (See Appendix C). Motivation to learn is assessed using 

items adapted from Hicks (1984). This measure is designed to evaluate how driven a candidate is 

perceived to be in acquiring new knowledge and skills. The instrument utilizes a 5-point Likert 

scale ranging from (1) Strongly Disagree to (5) Strongly Agree. Raters are asked to assess the 

extent to which they agree with statements reflecting the candidate’s enthusiasm for learning and 

development. Sample items include: “The candidate will be motivated to learn new skills,” and 

“The candidate will try to learn as much as they can.” This scale is constructed to reflect 

observable indicators of motivation as perceived by the rater. The measure demonstrated strong 

internal consistency in prior research and showed similarly robust reliability in the present 

sample (α = .80). Due to the two-item structure, omega was not computed. 

Perceived Suitability (See Appendix C). This measure is assessed with items adapted 

from (Fox et al., 1995). This measure shows to what extent the participants see the candidate as 

suitable for the position listed. The measure uses a 5-point Likert scale (1) Definitely not to (5) 

Definitely. Some example questions are: “Would you recommend employing this candidate in 

the IT department?” and “If this candidate was the only one who applied for the job, would you 

accept him?” Cronbach’s alpha value has been found to be reliable in the original scale (α =.89; 

Fox et al., 1995). In the present study, the suitability scale demonstrated good internal 

consistency (α = .89, ω = .91). 



25 

 

   
 

Succession, Identity, Consumption Prescriptive Ageism Scale (See Appendix C). 

This measure is assessed with items adapted from (North, 2013b). This measure shows to what 

extent the participants have perceived ageism. This is used as a covariate in each analysis.  The 

measure uses a 5-point Likert scale (1) strongly disagree to (5) strongly agree. Some example 

questions are: “Generally older people shouldn't go clubbing.” and “Younger people are usually 

more productive than older people at their jobs.” Cronbach’s alpha has been found to be reliable 

in the original scale (α =.93; North, 2013b). In the present study, the ageism scale demonstrated 

high internal consistency (α = .84, ω = .91)



Chapter 4 

RESULTS 

To reduce potential bias and improve statistical power, missing data were addressed 

using multiple imputation in R version 4.3.2 (R Core Team, 2023) with the mice package (van 

Buuren & Groothuis-Oudshoorn, 2011). The initial dataset consisted of 635 responses, which 

was reduced to 588 following data cleaning. Of the retained cases, 167 participants (27.6%) had 

missing data on at least one variable. Missingness rates ranged from 0.68% (competence) to 

7.14% (participant age), with warmth (1.36%), trainability (4.93%), suitability (4.76%), motive 

to train (1.70%), and ageism (6.97%) falling in between. Predictive Mean Matching (PMM) was 

used to generate 20 multiply imputed datasets with 20 iterations each to ensure convergence 

(Graham et al., 2007). Diagnostic checks confirmed stable convergence. All subsequent analyses 

were conducted on the pooled estimates using standard regression modeling procedures, which 

enhanced the accuracy and reproducibility of the results. 

Descriptive Statistics  

Descriptive statistics were computed for all demographic variables, and correlation 

analyses were conducted to examine the relationships between key constructs such as ageism, 

warmth, competence, and relevant outcomes. The results revealed several significant 

relationships among ageism, suitability, trainability, competence, warmth, and perceived 

technological ability. Ageism was positively correlated with trainability, r(586) = .35, p < .001, 

95% CI [0.28, 0.41], indicating that participants with higher ageist attitudes perceived candidates 

as more trainable. However, ageism was negatively associated with both suitability, r(586) = 

–.12, p = .003, 95% CI [–0.20, –0.04], and competence, r(586) = –.09, p = .035, 95% CI [–0.17, 

–0.01], suggesting that those with higher ageism were less likely to view candidates as 



27 

 

   
 

competent or suitable. Participant age was strongly negatively correlated with ageism, r(586) = 

–.45, p < .001, 95% CI [–0.51, –0.37], indicating that older individuals reported lower ageist 

attitudes. Moreover, participant age was negatively related to trainability, r(586) = –.17, p 

< .001, 95% CI [–0.23, –0.09], suggesting that older participants tended to view candidates as 

less trainable. However, participant age was not significantly associated with suitability, 

competence, or warmth (see Table 1). 

Trainability was negatively correlated with suitability, r(586) = –.26, p < .001, 95% CI [–

0.33, –0.18], and with competence, r(586) = –.11, p = .007, 95% CI [–0.19, –0.03], indicating 

that participants who viewed candidates as trainable were less likely to view them as suitable or 

competent overall. Competence was positively associated with both suitability, r(586) = .42, p 

< .001, 95% CI [0.35, 0.49], and warmth, r(586) = .26, p < .001, 95% CI [0.18, 0.33], suggesting 

that more competent candidates were also viewed as more suitable and warmer. Descriptive 

statistics and full correlations with confidence intervals are shown in Table 1.  

Manipulation Checks 

I asked two manipulation check questions to ensure participants accurately perceived the 

resume’s age, the candidate’s technological ability, and the school they attended. Approximately 

18% of participants in the “older” condition identified the candidate’s age as older (40+), while 

74% in the “younger” condition identified the candidates’ age as younger (< 40). Older 

participants (especially those aged 60–69, 58.3%) showed greater accuracy in identifying the 

intended resume cue, whereas younger and middle-aged groups scored below 50%. This pattern 

reinforces the need to control participant age in analyses, as age-related differences in task 

attention or interpretation could otherwise confound the observed effects. This might suggest that 

the manipulation was somewhat subtle across age groups, or that older participants were more 



28 

 

   
 

attentive to the age cue (Hauser et al., 2018). It is also possible that older participants were more 

familiar with age-stereotyped names like Earl, increasing their sensitivity to the implicit age 

manipulation (Social Security Administration, n.d.; Kleissner & Jahn, 2021). 

Approximately 58.3% of participants aged 60–69 demonstrated accuracy in identifying 

the intended resume cue, while accuracy rates among younger and middle-aged participants fell 

below 50%. Notably, the highest rates were observed in the younger condition and in the high-

tech ability condition, suggesting these cues were potentially more salient or more consistent 

with participants’ expectations. Results revealed a notable variation in participants’ ability to 

correctly identify the technological skill level of the candidate. Rates were highest among 

participants aged 18–29 (31.9%) and declined with age: 30–39 (28.8%), 40–49 (27.5%), 50–59 

(29.0%), 60–69 (16.7%), and 70–79 (0%). These findings suggest that older participants were 

less likely to detect the intended technology cue, reinforcing the importance of controlling for 

participant age in analyses to avoid confounding effects due to differential cue perception. This 

pattern may reflect a broader tendency for individuals to more readily identify cues that align 

with dominant age-related stereotypes, such as associating technological competence with youth 

leading to higher accuracy among younger participants (Charness et al., 2018; Comunello et al., 

2023). It is also possible that participants’ expectations were shaped by stereotype-consistent 

combinations of cues (e.g., younger age and tech ability), which may explain why older 

candidates with high-tech ability were less readily recognized.  

The age manipulation check was not correlated with competence, r(586) = 0.07, p = .082, 

95% CI [–0.01, 0.14], indicating a non-significant weak relationship between identifying the 

candidate’s implicit age cue and perceived competence. However, this correlation was small and 

not statistically significant, suggesting that the age manipulation may have been too subtle to 



29 

 

   
 

reliably influence competence perceptions. Similarly, the age manipulation check showed non-

significant associations with warmth, r(586) = 0.02, p = .605, 95% CI [–0.05, 0.09], trainability, 

r(586) = 0.04, p = .346, 95% CI [–0.04, 0.12], and suitability, r(586) = –0.00, p = .975, 95% CI 

[–0.08, 0.08], indicating that the age cue had minimal impact on these outcomes. 

In contrast, the technology manipulation check was positively associated with both 

competence, r(586) = .14, p = .001, 95% CI [.06, .22], and suitability, r(586) = .13, p = .002, 

95% CI [.05, .21], indicating that participants who correctly identified the candidate’s 

technological ability tended to rate them as more competent and suitable. However, technology 

ability was not significantly related to warmth, r(586) = .08, p = .061, 95% CI [–.01, .15], and 

was negatively but non-significantly associated with trainability, r(586) = –.07, p = .099, 95% CI 

[–.15, .01]. While none of these correlations exceeded a small effect size, the pattern suggests 

that perceived technological ability may subtly inform judgments of candidate competence and 

suitability more than warmth or trainability. 

Hypotheses Testing 

Missing data were handled using multiple imputation with 20 datasets and 20 iterations 

each, yielding stable parameter estimates and reducing potential bias due to missingness 

(Graham et al., 2007). For each hypothesis, models were estimated separately across imputations 

and then pooled to generate overall estimates of coefficients, standard errors, and 95% 

confidence intervals. All models controlled for ageism and participant age to account for 

potential rater bias and demographic variation, helping to isolate the effects of the experimental 

manipulations (Finkelstein et al., 1995; Posthuma & Campion, 2009). Linear regression models 

were used to test both direct and indirect effects, including mediation models where applicable. 



30 

 

   
 

This pooled estimation approach improves the accuracy and generalizability of results by 

incorporating both within- and between-imputation variability. 

Hypothesis 1 proposed that the candidate with the older implicit age cue would be 

perceived as warmer but less competent than the candidate with the younger age cue. However, 

results did not support this hypothesis. Implicit age cues did not significantly relate to either 

warmth (𝑏= 0.02, SE = 0.05, p = .663, 95% CI [-0.07, 0.11]) or competence (b = 0.05, SE = 0.04, 

p = .253, 95% CI [-0.04, 0.14]) perceptions. Ageism and participant age were not significant 

predictors of warmth, with estimates of b = -0.04, SE = 0.04, p = .398, 95% CI [-0.12, 0.05], and 

b = -0.00, SE = 0.00, p = .696, 95% CI [-0.01, 0.00], respectively. However, ageism significantly 

and negatively predicted competence (b = -0.10, SE = 0.04, p = .018, 95% CI [-0.17, -0.02]). 

These results are summarized in Table 2. 

 Hypotheses 2a and 2b examined the role of technology ability and its interaction with the 

implicit age cue (i.e., Resume Age) in predicting perceptions of warmth and competence. Table 3 

shows the parameter estimates. Hypothesis 2a proposed that candidates with technological ability 

would be perceived as less warm and more competent. In contrast to this prediction, technology 

ability significantly and positively predicted warmth, (b = 0.10, SE = 0.05, p = .031, 95% CI 

[0.01, 0.19]). It did not significantly predict competence; therefore, the hypothesis was not 

supported (b = 0.04, SE = 0.04, p = .330, 95% CI [–0.04, 0.13]).  

Hypothesis 2b proposed that the implicit age cue and technology ability would interact to 

influence perceptions. However, no significant interaction was found for warmth (𝑏= –0.01, SE = 

0.09, p = .888, 95% CI [–0.20, 0.17]) or competence (b = –0.01, SE = 0.09, p = .872, 95% CI [–

0.19, 0.16]). In both models, ageism and participant age were included as covariates and 



31 

 

   
 

remained largely non-significant (see Table 4), with the exception of ageism’s continued 

negative association with competence (b = –0.10, SE = 0.04, p = .017, 95% CI [–0.18, –0.02]).  

Hypothesis 3 proposed that the implicit age cue would negatively predict trainability 

perceptions (see Table 5 for parameter estimates). This hypothesis was not supported; implicit 

age cues did not significantly predict trainability, (b = 0.00, SE = 0.03, p = 1.00, 95% CI [–0.06, 

0.06]) However, ageism remained a strong and positive predictor of trainability (b = 0.17, SE = 

0.03, p < .001, 95% CI [0.11, 0.23]), whereas participant age was not a significant predictor (b = 

0.00, SE = 0.00, p = .666, 95% CI [–0.00, 0.00]). Hypothesis 3 also proposed that implicit age 

cues would negatively influence perceived suitability, this was not supported. Implicit age cues 

did not significantly predict suitability, (b = 0.03, SE = 0.05, p = .542, 95% CI [–0.07, 0.14]). 

Interestingly, ageism significantly negatively predicted suitability (b = –0.21, SE = 0.05, p 

< .001, 95% CI [–0.31, –0.12]), suggesting that participants with higher ageist beliefs rated 

candidates as less suitable. Participant age also emerged as a significant negative predictor of 

suitability (b = –0.01, SE = 0.00, p = <.001, 95% CI [–0.01, 0.00]).  

Hypothesis 4 posited that candidates with technological ability would be perceived as 

more trainable (see Table 6 for parameter estimates). This hypothesis was not supported. 

Technology ability did not significantly predict trainability (b = –0.03, SE = 0.03, p = .282, 95% 

CI [–0.10, 0.03]). Nonetheless, ageism continued to significantly predict higher trainability 

ratings (b = 0.17, SE = 0.03, p < .001, 95% CI [0.11, 0.23]), and participant age remained non-

significant (b = 0.00, SE = 0.00, p = .676, 95% CI [–0.00, 0.00]). Similarly, Hypothesis 4 

predicted that technology ability would positively predict perceived suitability, but this was also 

unsupported. Technology ability did not significantly predict suitability (b = 0.04, SE = 0.05, p 

= .453, 95% CI [–0.07, 0.15]). Consistent with previous models, ageism again significantly 



32 

 

   
 

negatively predicted suitability (b = –0.22, SE = 0.05, p < .001, 95% CI [–0.31, –0.12]). 

Participant age remained a significant negative predictor (b = –0.00, SE = 0.00, p = .088, 95% CI 

[–0.01, 0.00]).  

Hypotheses 5a through 5c examined whether perceptions of warmth and competence 

predicted trainability and suitability. Hypothesis 5a proposed that warmth would positively 

predict suitability (Table 7), but this effect was not significant, (b = 0.06, SE = 0.05, p = .230, 

95% CI [–0.04, 0.15]). In this model, ageism remained a significant negative predictor of 

suitability (b = –0.21, SE = 0.05, p < .001, 95% CI [–0.31, –0.11]), while participant age was not 

a significant predictor (b = –0.00, SE = 0.00, p = .090, 95% CI [–0.01, 0.00]). Hypothesis 5b, 

which predicted that warmth would be associated with trainability (see Table 8 for parameter 

estimates), was also unsupported (b = –0.01, SE = 0.03, p = .633, 95% CI [–0.07, 0.04]). 

However, ageism remained a significant positive predictor of trainability (b = 0.17, SE = 0.03, p 

< .001, 95% CI [0.11, 0.22]), while participant age was not a significant predictor (b = 0.00, SE = 

0.00, p = .673, 95% CI [–0.00, 0.00]). 

Hypothesis 5c, which posited that competence would positively predict both outcomes, 

was supported (Table 9). Competence significantly predicted both trainability (𝑏= 0.47, SE = 

0.05, p < .001, 95% CI [0.38, 0.57]), and suitability (b = 0.47, SE = 0.05, p < .001, 95% CI [0.38, 

0.57]). However, ageism did not significantly predict suitability (b = –0.00, SE = 0.05, p = .967, 

95% CI [–0.09, 0.09]) or trainability, (b = –0.00, SE = 0.00, p = .076, 95% CI [–0.01, 

0.00]).These results suggest that as perceptions of competence increased, so did evaluations of 

the candidate’s trainability and suitability for the role. The effects of participant age were not 

significant in these models, indicating that age did not meaningfully alter perceptions of 

candidate trainability or suitability when controlling for competence and warmth. 



33 

 

   
 

Hypothesis 6 initially tested a moderated mediation model in which implicit age cues and 

perceived technology ability were expected to influence trainability and suitability indirectly 

through warmth. This model was estimated using a path analysis framework implemented in the 

lavaan package in R (Rosseel, 2012). However, the interaction between implicit age cues and 

perceived technology ability was not statistically significant. In accordance with 

recommendations for model simplification (Hayes, 2017), the model was refit as a simple 

mediation model excluding the interaction term, to evaluate whether warmth mediated the 

relationship between implicit age cues and hiring outcomes. 

Conditional indirect effects of implicit age cues on trainability and suitability through 

warmth were not statistically significant at either low or high levels of perceived technology 

ability. When technology ability was low, the indirect effect of age cues on trainability via 

warmth was nonsignificant (b = −0.00, SE = 0.00, p = .956, 95% CI [−0.00, 0.00]); the indirect 

effect remained nonsignificant when technology ability was high (b = −0.00, SE = 0.00, p = .795, 

95% CI [−0.01, 0.00]). A similar pattern was observed for suitability, with no significant 

conditional indirect effects when technology ability was low (b = 0.00, SE = 0.00, p = .956) or 

high (b = 0.00, SE = 0.01, p = .560). The index of moderated mediation was also nonsignificant 

for both trainability (b = −0.00, SE = 0.00, p = .796) and suitability (b = 0.00, SE = 0.01, p = 

.568), indicating that the strength of the indirect effect through warmth did not differ by levels of 

perceived technology ability. 

To further examine the potential indirect effect of implicit age cues via warmth, the 

model was refit as a standard mediation model excluding the interaction term. Resume age did 

not significantly predict warmth (b = 0.02, SE = 0.05, p = .708, 95% CI [−0.07, 0.11]), and 

warmth did not significantly predict trainability (b = −0.02, SE = 0.03, p = .495, 95% CI [−0.08, 



34 

 

   
 

0.04]; see Table 10). The indirect effect of resume age on trainability through warmth was also 

not significant (b = −0.00, SE = 0.00, p = .752, 95% CI [−0.00, 0.00]), and the total effect 

remained nonsignificant (b = 0.01, SE = 0.03, p = .811, 95% CI [−0.06, 0.07]). 

A similar pattern emerged for suitability. Resume age again did not significantly predict 

warmth (b = 0.02, SE = 0.05, p = .708, 95% CI [−0.07, 0.11]), and warmth was not significantly 

associated with suitability (b = 0.06, SE = 0.05, p = .239, 95% CI [−0.04, 0.15]; see Table 11). 

The indirect effect of resume age on suitability via warmth was not significant (b = 0.00, SE = 

0.00, p = .724, 95% CI [−0.00, 0.01]), and the total effect was also nonsignificant (b = 0.02, SE = 

0.05, p = .650, 95% CI [−0.08, 0.13]). Overall, these results do not support a mediating role of 

warmth in the relationship between resume age and either trainability or suitability. 

Hypothesis 6 also tested a second moderated mediation model in which implicit age cues 

and perceived technology ability were expected to influence trainability and suitability indirectly 

through competence. In this model, resume age, perceived technology ability, and their 

interaction were modeled as predictors of perceived competence, which was in turn expected to 

influence downstream hiring outcomes. Conditional indirect effects of resume age on trainability 

and suitability through competence were not statistically significant at either low or high levels 

of perceived technology ability. For trainability, the indirect effect was nonsignificant when 

technology ability was low (b = −0.01, SE = 0.01, p = .320, 95% CI [−0.02, 0.01]) or high (b = 

−0.01, SE = 0.01, p = .148, 95% CI [−0.02, 0.00]). For suitability, conditional indirect effects 

were also nonsignificant at both low (b = 0.04, SE = 0.04, p = .285, 95% CI [−0.03, 0.11]) and 

high (b = 0.06, SE = 0.03, p = .087, 95% CI [−0.01, 0.13]) levels of perceived technology ability. 

The indices of moderated mediation were not significant for either trainability (b = −0.01, SE = 

0.01, p = .561) or suitability (b = 0.02, SE = 0.04, p = .551), suggesting that the strength of 



35 

 

   
 

indirect effects via competence did not vary as a function of technology ability. Given these 

findings, the interaction term was removed and the model was refit as a standard mediation 

model to evaluate the indirect effect of resume age on hiring outcomes through competence. 

To further examine whether competence functioned as a mediator, the model was refit as 

a simple mediation model, excluding the interaction term. Resume age did not significantly 

predict competence (b = 0.05, SE = 0.04, p = .263, 95% CI [−0.04, 0.14]; see Table 12), and 

competence significantly predicted lower ratings of trainability (b = −0.10, SE = 0.03, p = .003, 

95% CI [−0.15, −0.04] see Table 11). However, the indirect effect of resume age on trainability 

through competence was not significant (b = −0.00, SE = 0.00, p = .292, 95% CI [−0.01, 0.00]), 

and the total effect remained nonsignificant (b = 0.01, SE = 0.03, p = .811, 95% CI [−0.06, 

0.07]). 

For suitability, resume age again did not significantly predict competence (b = 0.05, SE = 

0.04, p = .263), but competence was a strong positive predictor of suitability (b = 0.55, SE = 

0.04, p < .001, 95% CI [0.46, 0.63]; see Table 13). The indirect effect of resume age on 

suitability via competence was not statistically significant (b = 0.03, SE = 0.02, p = .265, 95% CI 

[−0.02, 0.07]), and the total effect was also nonsignificant (b = 0.02, SE = 0.05, p = .650, 95% CI 

[−0.08, 0.13]). These results indicate that, although competence was strongly related to hiring 

judgments, it did not mediate the effect of implicit age cues on either trainability or suitability 

when modeled without interaction. 

Supplemental Analyses  

To further examine candidate evaluations, supplemental analyses were conducted using 

motive-based trainability in place of the original trainability measure. These models mirrored the 

structure of the primary analyses and included implicit age cues, perceived technology ability, 



36 

 

   
 

warmth, competence, and covariates such as participant age and ageism. This approach allowed 

for exploration of whether motivational aspects of trainability—such as willingness and effort to 

improve—were influenced differently than perceptions of general trainability. Results largely 

paralleled those of the primary models, with competence emerging as the most consistent 

predictor across outcomes. However, the similarity in findings may partially reflect common 

method variance, as trainability and motive-to-train items were presented in succession within 

the survey. 

Hypothesis 3 tested whether implicit age cues predicted motive-based trainability. 

Resume age was not a significant predictor of motive-based trainability (b = −0.03, SE = 0.04, 

95% CI [−0.10, 0.04], p = .410). Ageism was a significant negative predictor (b = −0.13, SE = 

0.03, 95% CI [−0.19, −0.06], p < .001), indicating that participants higher in ageist beliefs rated 

candidates as less motivated to be trained. Participant age was not significant (b = −0.00, SE = 

0.00, 95% CI [−0.00, 0.00], p = .963). Hypothesis 4 examined whether perceived technology 

ability predicted motive-based trainability. Resume technology ability did not significantly 

predict motive-based trainability (b = 0.05, SE = 0.04, 95% CI [−0.02, 0.12], p = .148). As in 

Hypothesis 3, ageism remained a significant negative predictor (b = −0.13, SE = 0.03, 95% CI 

[−0.20, −0.07], p < .001). Participant age was not significant (b = −0.00, SE = 0.00, 95% CI 

[−0.00, 0.00], p = .960). 

Hypothesis 5b tested whether warmth predicted motive-based trainability. The effect of 

warmth was in the expected positive direction but was not significant (b = 0.00, SE = 0.03, 95% 

CI [−0.06, 0.07], p = .941). Ageism significantly predicted lower motive-based trainability (b = 

−0.13, SE = 0.03, 95% CI [−0.19, −0.06], p < .001), while participant age was nonsignificant (b 

= −0.00, SE = 0.00, 95% CI [−0.00, 0.00], p = .945). Hypothesis 5c tested whether competence 



37 

 

   
 

predicted both motive-based trainability and suitability. Competence significantly predicted 

motive-based trainability (b = 0.23, SE = 0.03, 95% CI [0.16, 0.29], p < .001) and suitability (b = 

0.53, SE = 0.05, 95% CI [0.44, 0.62], p < .001). Ageism also significantly predicted both 

outcomes (trainability: b = −0.11, SE = 0.03, 95% CI [−0.17, −0.04], p = .001; suitability: b = 

−0.16, SE = 0.04, 95% CI [−0.25, −0.08], p < .001). Participant age significantly predicted lower 

suitability (b = −0.01, SE = 0.00, 95% CI [−0.01, −0.00], p = .025) but not motive-based 

trainability (b = −0.00, SE = 0.00, 95% CI [−0.00, 0.00], p = .845). 

Hypothesis 6 tested two distinct moderated mediation models in which implicit age cues 

and perceived technology ability were expected to influence candidate evaluations indirectly 

through warmth or competence. In the warmth pathway model, conditional indirect effects of 

resume age on both motive-based trainability and suitability through warmth were not 

statistically significant. For motive-based trainability, the conditional indirect effect was 

nonsignificant when perceived technology ability was low (b = −0.00, SE = 0.00, p = .956) or 

high (b = −0.01, SE = 0.00, p = .795). Similarly, the conditional indirect effects for suitability 

were also nonsignificant when technology ability was low (b = 0.00, SE = 0.00, p = .956) or high 

(b = 0.00, SE = 0.01, p = .560). The indices of moderated mediation were not significant for 

either outcome (trainability: b = −0.01, SE = 0.01, p = .796; suitability: b = 0.00, SE = 0.01, p 

= .568), indicating that the strength of the indirect effects through warmth did not vary as a 

function of perceived technology ability. 

Perceived technology ability was not a significant predictor of warmth (b = 0.11, SE = 

0.07, 95% CI [−0.02, 0.24], p = .100), and neither implicit age cues (b = 0.00, SE = 0.07, 95% CI 

[−0.14, 0.14], p = .955) nor the interaction between age cues and technology ability (b = −0.03, 

SE = 0.10, 95% CI [−0.21, 0.16], p = .795) significantly predicted warmth. The perceived 



38 

 

   
 

warmth was also not significantly associated with motive-based trainability (b = −0.01, SE = 

0.03, p = .791) or suitability (b = 0.03, SE = 0.05, p = .518). Together, these findings suggest that 

warmth did not mediate or moderate the relationship between implicit age cues and candidate 

evaluations. 

To further probe whether warmth served as a mediator in the absence of significant 

moderation, two simple mediation models were tested. In the motive-based trainability model, 

resume age did not significantly predict perceived warmth (b = 0.01, SE = 0.05, p = .809), and 

warmth was not significantly associated with motive-based trainability (b = 0.01, SE = 0.03, p 

= .845). The indirect effect of resume age on motive-based trainability through warmth was 

nonsignificant (b = 0.00, SE = 0.00, p = .879), and the total effect was also nonsignificant (b = 

−0.03, SE = 0.04, p = .399).  

Similarly, in the suitability model, resume age did not predict warmth (b = −0.00, SE = 

0.05, p = .937), and warmth did not predict suitability (b = 0.05, SE = 0.05, p = .318). The 

indirect effect of resume age on suitability through warmth was not significant (b = −0.00, SE = 

0.00, p = .937), nor was the total effect (b = 0.02, SE = 0.06, p = .689). These results reinforce 

the conclusion that perceived warmth was not a significant explanatory mechanism linking 

implicit age cues to either motive-based trainability or suitability. 

In the competence pathway model, conditional indirect effects of resume age on both 

motive-based trainability and suitability through competence were not statistically significant. 

For motive-based trainability, the conditional indirect effect was nonsignificant when perceived 

technology ability was low (b = 0.02, SE = 0.02, p = .288) or high (b = 0.03, SE = 0.02, p 

= .092). Similarly, the conditional indirect effects for suitability were also nonsignificant at low 

(b = 0.04, SE = 0.04, p = .285) and high (b = 0.06, SE = 0.03, p = .087) levels of technology 



39 

 

   
 

ability. The indices of moderated mediation were not significant for either outcome (trainability: 

b = 0.01, SE = 0.02, p = .552; suitability: b = 0.02, SE = 0.04, p = .551), indicating that the 

strength of the indirect effects through competence did not significantly vary as a function of 

perceived technology ability. 

Resume age (b = 0.07, SE = 0.06, p = .283, 95% CI [−0.06, 0.19]), technology ability (b 

= 0.07, SE = 0.06, p = .252; 95% CI [−0.05, 0.19]), and their interaction (b = −0.03, SE = 0.09, p 

= .721, 95% CI [−0.21, 0.14]) were not significant predictors of perceived competence. 

However, perceived competence was significantly associated with both motive-based trainability 

(b = 0.24, SE = 0.03, p < .001) and suitability (b = 0.56, SE = 0.05, p < .001), indicating that 

higher competence ratings were strongly related to more favorable evaluations, even though no 

indirect effects emerged from the manipulated cues. 

When the interaction term was removed, a simplified mediation model was estimated to 

test whether perceived competence mediated the relationship between resume age and candidate 

evaluations. In the trainability model, resume age was not a significant predictor of perceived 

competence (b = 0.06, SE = 0.04, p = .176), and the indirect effect of resume age on motive-

based trainability through competence was nonsignificant (b = 0.02, SE = 0.01, p = .183). 

Although perceived competence was a significant predictor of motive-based trainability (b = 

0.24, SE = 0.03, p < .001), the overall model explained only 9% of the variance in motive to 

train. 

In the suitability model, age again did not significantly predict competence (b = 0.05, SE 

= 0.04, p = .236), and the indirect effect of resume age on suitability through competence was 

nonsignificant (b = 0.03, SE = 0.03, p = .238). Perceived competence remained a strong predictor 

of suitability (b = 0.57, SE = 0.05, p < .001), accounting for nearly 20% of the variance in 



40 

 

   
 

suitability ratings. Taken together, these results indicate that although competence significantly 

influenced hiring outcomes, it did not mediate the relationship between resume age and 

candidate evaluations in this simplified model. 



Chapter 5 

DISCUSSION 

This study reinforces the importance of competence in shaping early-stage hiring judgments and 

offers a more nuanced understanding of the limited role that warmth plays in resume evaluations. 

While perceived technology ability was associated with increased perceptions of warmth, 

implicit age cues and their interaction did not significantly influence perceptions of either 

warmth or competence. Only competence consistently predicted candidate evaluations, 

significantly influencing both trainability and suitability ratings. These results suggest that in 

task-focused hiring contexts, such as consulting roles where technical aptitude, strategic 

thinking, and adaptability are prioritized, competence may serve as a more salient dimension of 

evaluation than interpersonal qualities like warmth. 

In contrast, neither implicit age cues nor perceived technological ability significantly 

predicted suitability when controlling for participant age and ageism. This mirrors the null results 

observed for trainability, suggesting that neither cue on its own was sufficient to shape 

downstream judgments of role fit or potential. However, ageism emerged as a significant 

negative predictor of suitability, such that participants with stronger ageist beliefs rated 

candidates as less suitable for the role. Participant age also negatively predicted suitability. These 

findings suggest that evaluators’ biases and demographic characteristics, rather than the resume 

manipulations themselves, played a larger role in shaping final hiring outcomes. 

Warmth and Competence in Hiring Evaluations  

This study applied the Stereotype Content Model (SCM; Fiske et al., 2002) to examine 

how perceptions of warmth and competence influence hiring judgments in response to implicit 

age cues and perceived technological ability. Consistent with prior research, competence 



42 

 

   
 

emerged as a robust predictor of hiring-relevant outcomes. Candidates perceived as more 

competent were rated as more suitable for the role and more capable of being trained, reinforcing 

the centrality of competence in employment decision-making processes (Cuddy et al., 2008; Lin 

et al., 2005). This aligns with research suggesting that competence reflects perceived task-

relevant ability, which is often prioritized in evaluations of fit and productivity. 

In contrast, warmth did not significantly predict either suitability or trainability, diverging 

from some previous findings and classic SCM predictions (Abrams et al., 2016; Fiske et al., 

2002). While warmth is typically valued in interpersonal and team-based contexts, it may carry 

less evaluative weight when assessing performance in technical or specialized roles. This 

suggests that while warmth may shape general impressions, it may not play a decisive role in 

downstream hiring decisions where perceived job performance is paramount (Cuddy et al., 2008; 

Lin et al., 2005). These results refine the SCM's application in employment settings, highlighting 

how context shapes the relative importance of warmth versus competence. 

Notably, implicit age cues did not significantly influence perceptions of warmth or 

competence. Only perceived technological ability was associated with greater perceived warmth, 

this may have occurred because technological ability signaled traits such as openness, 

adaptability, and enthusiasm qualities that align closely with warmth in the Stereotype Content 

Model (Fiske, 2002; Cuddy et al., 2008). While the interaction between age and technology cues 

was non-significant. While this suggests that certain resume cues may shape interpersonal 

impressions, these impressions did not translate into downstream effects on hiring-relevant 

outcomes such as trainability or suitability. Warmth did not mediate the effects of implicit age or 

technology cues, and although competence significantly predicted hiring evaluations, it was not 

shaped by the resume manipulations. These findings offer limited support for the moderated 



43 

 

   
 

mediation framework proposed, highlighting the need for future research to further examine the 

conditions under which interpersonal perceptions function as mechanisms linking implicit cues 

to hiring outcomes (O’Laughlin et al., 2018; Preacher, 2015).  

Together, these findings suggest that while competence continues to play a central role in 

shaping hiring judgments, warmth does not serve as a significant pathway through which age-

related or technological cues affect evaluations. Future research should explore alternative 

mediators that may better explain how implicit cues influence hiring judgments in consulting 

roles. Constructs like perceived trainability, motivation, or role fit may be more relevant than 

warmth, as they reflect qualities valued in fast-paced, adaptable work environments. The 

hypothesized indirect and conditional indirect effects were unsupported, and neither implicit age 

cues nor perceived technology ability consistently shaped perceptions in a way that altered 

evaluations of candidate trainability or suitability. These results emphasize the primacy of 

competence in personnel selection and highlight the complexity of translating subtle social cues 

into evaluative judgments. 

Age and Ageism Perceptions  

Participant age and ageist beliefs emerged as important predictors of candidate 

evaluations. These variables were included as covariates in all models to account for individual 

differences in rater bias and demographic context. Controlling for participant age and ageism 

helped isolate the effects of implicit age cues and perceived technology ability, ensuring that 

observed relationships were driven by the resume manipulations rather than evaluators’ 

predispositions (Posthuma & Campion, 2009). 

Consistent with previous literature, participants who held stronger ageist beliefs were 

more likely to rate candidates as less suitable and less competent (Abrams et al., 2016) yet 



44 

 

   
 

paradoxically rated them as more trainable. This pattern may reflect prescriptive ageism, societal 

expectations that older individuals should disengage from career advancement or step aside for 

younger generations (North & Fiske, 2013b). Rather than seeing older candidates as promising, 

ageist raters may view them as currently lacking key skills, but still capable of being reshaped or 

corrected through training. In this sense, higher ratings of trainability may reflect a belief that 

older workers require remediation to meet role expectations, not confidence in their long-term 

potential (North & Fiske, 2013b; Finkelstein, 2014). 

Participant age was also a relevant factor in candidate evaluations. In earlier analyses of 

the manipulation checks, older participants were more likely to correctly identify the implicit age 

cue, likely due to greater familiarity with names that were more common in earlier decades (e.g., 

Earl, a name drawn from the 1950s). However, they were less accurate in detecting the 

candidate’s technological ability, which may reflect differences in stereotype salience or 

familiarity with tech-related language (Birkland, 2022; Ivan & Cutler, 2021). These findings 

underscore the practical importance of controlling for participant age in statistical models to 

reduce the risk of confounding effects related to differential cue perception and interpretation. 

Theoretical Implications 

This study contributes to the growing literature on age-based stereotyping in hiring by 

extending the Stereotype Content Model (SCM; Fiske et al., 2002) and prescriptive ageism 

theory (North & Fiske, 2013b) to the context of resume screening. Specifically, it examined 

whether implicit age cues influenced candidate evaluations of trainability and suitability through 

perceived warmth and competence, and whether perceived technology ability moderated these 

effects. By incorporating both implicit cues and rater characteristics, this research offers a more 

nuanced understanding of how individual and contextual factors jointly shape hiring judgments. 



45 

 

   
 

In line with SCM, competence emerged as a central dimension influencing downstream 

hiring outcomes. Participants rated more competent candidates as both more trainable and more 

suitable. This aligns with prior research underscoring competence as a critical criterion in task-

oriented evaluations (Cuddy et al., 2008; Hashim & Wok, 2012), especially in consulting roles 

that demand adaptability, technical problem-solving, and the ability to deliver client-facing 

results. In these contexts, competence signals not only job readiness but also developmental 

potential—making it a more decisive factor than interpersonal traits. 

However, warmth did not significantly predict trainability or suitability, diverging from 

SCM’s assertion that both warmth and competence guide evaluative judgments (Fiske et al., 

2002). This suggests that in performance